upload hubscripts/bioasq_2021_mesinesp_hub.py to hub from bigbio repo

bioasq_2021_mesinesp.py

@@ -0,0 +1,315 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+The main aim of MESINESP2 is to promote the development of practically relevant
+semantic indexing tools for biomedical content in non-English language. We have
+generated a manually annotated corpus, where domain experts have labeled a set
+of scientific literature, clinical trials, and patent abstracts. All the
+documents were labeled with DeCS descriptors, which is a structured controlled
+vocabulary created by BIREME to index scientific publications on BvSalud, the
+largest database of scientific documents in Spanish, which hosts records from
+the databases LILACS, MEDLINE, IBECS, among others.
+
+MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS
+to different types of biomedical documents. To that purpose, we have divided the
+task into three subtracks depending on the document type. Then, for each one we
+generated an annotated corpus which was provided to participating teams:
+
+- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all
+  Spanish records from LILACS and IBECS databases at the Virtual Health Library
+  (VHL) with non-empty abstract written in Spanish.
+- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro
+  Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the
+  structure title/abstract needed in BioASQ, for that reason we have built
+  artificial abstracts based on the content available in the data crawled using
+  the REEC API.
+- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in
+  Spanish extracted from Google Patents which have the IPC code “A61P” and
+  “A61K31”. In addition, we also provide a set of complementary data such as:
+  the DeCS terminology file, a silver standard with the participants' predictions
+  to the task background set and the entities of medications, diseases, symptoms
+  and medical procedures extracted from the BSC NERs documents.
+"""
+
+import json
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import text.features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['Spanish']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@conference {396,
+    title = {Overview of BioASQ 2021-MESINESP track. Evaluation of
+    advance hierarchical classification techniques for scientific
+    literature, patents and clinical trials.},
+    booktitle = {Proceedings of the 9th BioASQ Workshop
+    A challenge on large-scale biomedical semantic indexing
+    and question answering},
+    year = {2021},
+    url = {http://ceur-ws.org/Vol-2936/paper-11.pdf},
+    author = {Gasco, Luis and Nentidis, Anastasios and Krithara, Anastasia
+     and Estrada-Zavala, Darryl and Toshiyuki Murasaki, Renato and Primo-Pe{\~n}a,
+     Elena and Bojo-Canales, Cristina and Paliouras, Georgios and Krallinger, Martin}
+}
+
+"""
+
+_DATASETNAME = "bioasq_2021_mesinesp"
+_DISPLAYNAME = "MESINESP 2021"
+
+_DESCRIPTION = """\
+The main aim of MESINESP2 is to promote the development of practically relevant \
+semantic indexing tools for biomedical content in non-English language. We have \
+generated a manually annotated corpus, where domain experts have labeled a set \
+of scientific literature, clinical trials, and patent abstracts. All the \
+documents were labeled with DeCS descriptors, which is a structured controlled \
+vocabulary created by BIREME to index scientific publications on BvSalud, the \
+largest database of scientific documents in Spanish, which hosts records from \
+the databases LILACS, MEDLINE, IBECS, among others.
+
+MESINESP track at BioASQ9 explores the efficiency of systems for assigning DeCS \
+to different types of biomedical documents. To that purpose, we have divided the \
+task into three subtracks depending on the document type. Then, for each one we \
+generated an annotated corpus which was provided to participating teams:
+
+- [Subtrack 1 corpus] MESINESP-L – Scientific Literature: It contains all \
+  Spanish records from LILACS and IBECS databases at the Virtual Health Library \
+  (VHL) with non-empty abstract written in Spanish.
+- [Subtrack 2 corpus] MESINESP-T- Clinical Trials contains records from Registro \
+  Español de Estudios Clínicos (REEC). REEC doesn't provide documents with the \
+  structure title/abstract needed in BioASQ, for that reason we have built \
+  artificial abstracts based on the content available in the data crawled using \
+  the REEC API.
+- [Subtrack 3 corpus] MESINESP-P – Patents: This corpus includes patents in \
+  Spanish extracted from Google Patents which have the IPC code “A61P” and \
+  “A61K31”. In addition, we also provide a set of complementary data such as: \
+  the DeCS terminology file, a silver standard with the participants' predictions \
+  to the task background set and the entities of medications, diseases, symptoms \
+  and medical procedures extracted from the BSC NERs documents.
+"""
+
+_HOMEPAGE = "https://zenodo.org/record/5602914#.YhSXJ5PMKWt"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLS = {
+    _DATASETNAME: {
+        "subtrack1": "https://zenodo.org/record/5602914/files/Subtrack1-Scientific_Literature.zip?download=1",
+        "subtrack2": "https://zenodo.org/record/5602914/files/Subtrack2-Clinical_Trials.zip?download=1",
+        "subtrack3": "https://zenodo.org/record/5602914/files/Subtrack3-Patents.zip?download=1",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]
+
+_SOURCE_VERSION = "1.0.6"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class Bioasq2021MesinespDataset(datasets.GeneratorBasedBuilder):
+    """\
+    A dataset to promote the development of practically relevant
+    semantic indexing tools for biomedical content in non-English language.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack1_all_source",
+            version=SOURCE_VERSION,
+            description="bioasq_2021_mesinesp source schema subtrack1",
+            schema="source",
+            subset_id="bioasq_2021_mesinesp_subtrack1_all",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack1_only_articles_source",
+            version=SOURCE_VERSION,
+            description="bioasq_2021_mesinesp source schema subtrack1",
+            schema="source",
+            subset_id="bioasq_2021_mesinesp_subtrack1_only_articles",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack2_source",
+            version=SOURCE_VERSION,
+            description="bioasq_2021_mesinesp source schema subtrack2",
+            schema="source",
+            subset_id="bioasq_2021_mesinesp_subtrack2",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack3_source",
+            version=SOURCE_VERSION,
+            description="bioasq_2021_mesinesp source schema subtrack3",
+            schema="source",
+            subset_id="bioasq_2021_mesinesp_subtrack3",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack1_all_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="bioasq_2021_mesinesp BigBio schema subtrack1",
+            schema="bigbio_text",
+            subset_id="bioasq_2021_mesinesp_subtrack1_all",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack1_only_articles_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="bioasq_2021_mesinesp BigBio schema subtrack1",
+            schema="bigbio_text",
+            subset_id="bioasq_2021_mesinesp_subtrack1_only_articles",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack2_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="bioasq_2021_mesinesp BigBio schema subtrack2",
+            schema="bigbio_text",
+            subset_id="bioasq_2021_mesinesp_subtrack2",
+        ),
+        BigBioConfig(
+            name="bioasq_2021_mesinesp_subtrack3_bigbio_text",
+            version=BIGBIO_VERSION,
+            description="bioasq_2021_mesinesp BigBio schema subtrack3",
+            schema="bigbio_text",
+            subset_id="bioasq_2021_mesinesp_subtrack3",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "bioasq_2021_mesinesp_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "abstractText": datasets.Value("string"),
+                    "db": datasets.Value("string"),
+                    "decsCodes": datasets.Sequence(datasets.Value("string")),
+                    "id": datasets.Value("string"),
+                    "journal": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                }
+            )
+        elif self.config.schema == "bigbio_text":
+            features = text.features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        if "subtrack1" in self.config.name:
+            track = "1"
+        elif "subtrack2" in self.config.name:
+            track = "2"
+        else:
+            track = "3"
+
+        urls = _URLS[_DATASETNAME][f"subtrack{track}"]
+        if self.config.data_dir is None:
+            try:
+                data_dir = dl_manager.download_and_extract(urls)
+            except ConnectionError:
+                raise ConnectionError(
+                    "Could not download. Save locally and use `data_dir` kwarg"
+                )
+        else:
+            data_dir = self.config.data_dir
+
+        if track == "1":
+            top_folder = "Subtrack1-Scientific_Literature"
+        elif track == "2":
+            top_folder = "Subtrack2-Clinical_Trials"
+        else:
+            top_folder = "Subtrack3-Patents"
+        if track == "1":
+            if "all" in self.config.name:
+                train_filepath = "training_set_subtrack1_all.json"
+            else:
+                train_filepath = "training_set_subtrack1_only_articles.json"
+        else:
+            train_filepath = f"training_set_subtrack{track}.json"
+
+        dev_filepath = f"development_set_subtrack{track}.json"
+        test_filepath = f"test_set_subtrack{track}.json"
+
+        split_gens = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, top_folder, "Train", train_filepath
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, top_folder, "Development", dev_filepath
+                    ),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(
+                        data_dir, top_folder, "Test", test_filepath
+                    ),
+                },
+            ),
+        ]
+
+        # track 3 doesn't have Train data
+        if track == "3":
+            return split_gens[1:]
+
+        return split_gens
+
+    def _generate_examples(self, filepath) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+
+            with open(filepath) as fp:
+                data = json.load(fp)
+
+            for key, example in enumerate(data["articles"]):
+                yield key, example
+
+        elif self.config.schema == "bigbio_text":
+            with open(filepath) as fp:
+                data = json.load(fp)
+
+            for key, example in enumerate(data["articles"]):
+                yield key, {
+                    "id": example["id"],
+                    "document_id": "NULL",
+                    "text": example["abstractText"],
+                    "labels": example["decsCodes"],
+                }